Overview

Dataset statistics

Number of variables14
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory109.5 KiB
Average record size in memory112.1 B

Variable types

Numeric6
Categorical8

Alerts

restingBP is highly overall correlated with targetHigh correlation
serumcholestrol is highly overall correlated with targetHigh correlation
chestpain is highly overall correlated with targetHigh correlation
slope is highly overall correlated with targetHigh correlation
noofmajorvessels is highly overall correlated with targetHigh correlation
target is highly overall correlated with restingBP and 4 other fieldsHigh correlation
patientid has unique valuesUnique
serumcholestrol has 53 (5.3%) zerosZeros
oldpeak has 19 (1.9%) zerosZeros

Reproduction

Analysis started2023-10-09 23:39:32.241348
Analysis finished2023-10-09 23:39:45.892653
Duration13.65 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

patientid
Real number (ℝ)

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5048704.4
Minimum103368
Maximum9990855
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-10-09T23:39:46.076886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum103368
5-th percentile668284.25
Q12536439.5
median4952508.5
Q37681877
95-th percentile9473476.5
Maximum9990855
Range9887487
Interquartile range (IQR)5145437.5

Descriptive statistics

Standard deviation2895904.5
Coefficient of variation (CV)0.57359359
Kurtosis-1.2669058
Mean5048704.4
Median Absolute Deviation (MAD)2571651
Skewness0.020316773
Sum5.0487044 × 109
Variance8.3862629 × 1012
MonotonicityStrictly increasing
2023-10-09T23:39:46.385726image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
103368 1
 
0.1%
6865627 1
 
0.1%
6757890 1
 
0.1%
6769686 1
 
0.1%
6781392 1
 
0.1%
6784916 1
 
0.1%
6814546 1
 
0.1%
6828182 1
 
0.1%
6833440 1
 
0.1%
6839487 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
103368 1
0.1%
119250 1
0.1%
119372 1
0.1%
132514 1
0.1%
146211 1
0.1%
148462 1
0.1%
168686 1
0.1%
170498 1
0.1%
188225 1
0.1%
192523 1
0.1%
ValueCountFrequency (%)
9990855 1
0.1%
9988507 1
0.1%
9965859 1
0.1%
9953423 1
0.1%
9949544 1
0.1%
9937998 1
0.1%
9921653 1
0.1%
9911700 1
0.1%
9896438 1
0.1%
9888918 1
0.1%

age
Real number (ℝ)

Distinct61
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.242
Minimum20
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-10-09T23:39:46.678533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile22
Q134
median49
Q364.25
95-th percentile77
Maximum80
Range60
Interquartile range (IQR)30.25

Descriptive statistics

Standard deviation17.86473
Coefficient of variation (CV)0.36279457
Kurtosis-1.2228314
Mean49.242
Median Absolute Deviation (MAD)15
Skewness0.028407812
Sum49242
Variance319.14858
MonotonicityNot monotonic
2023-10-09T23:39:46.953986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 28
 
2.8%
58 23
 
2.3%
24 22
 
2.2%
76 22
 
2.2%
73 21
 
2.1%
46 21
 
2.1%
43 21
 
2.1%
44 21
 
2.1%
45 21
 
2.1%
51 20
 
2.0%
Other values (51) 780
78.0%
ValueCountFrequency (%)
20 28
2.8%
21 12
1.2%
22 19
1.9%
23 19
1.9%
24 22
2.2%
25 20
2.0%
26 18
1.8%
27 15
1.5%
28 12
1.2%
29 16
1.6%
ValueCountFrequency (%)
80 12
1.2%
79 14
1.4%
78 15
1.5%
77 17
1.7%
76 22
2.2%
75 11
1.1%
74 20
2.0%
73 21
2.1%
72 15
1.5%
71 18
1.8%

gender
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1
765 
0
235 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 765
76.5%
0 235
 
23.5%

Length

2023-10-09T23:39:47.218381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-09T23:39:47.451833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 765
76.5%
0 235
 
23.5%

Most occurring characters

ValueCountFrequency (%)
1 765
76.5%
0 235
 
23.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 765
76.5%
0 235
 
23.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 765
76.5%
0 235
 
23.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 765
76.5%
0 235
 
23.5%

chestpain
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0
420 
2
312 
1
224 
3
44 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row0
3rd row2
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 420
42.0%
2 312
31.2%
1 224
22.4%
3 44
 
4.4%

Length

2023-10-09T23:39:47.687620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-09T23:39:47.947741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 420
42.0%
2 312
31.2%
1 224
22.4%
3 44
 
4.4%

Most occurring characters

ValueCountFrequency (%)
0 420
42.0%
2 312
31.2%
1 224
22.4%
3 44
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 420
42.0%
2 312
31.2%
1 224
22.4%
3 44
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 420
42.0%
2 312
31.2%
1 224
22.4%
3 44
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 420
42.0%
2 312
31.2%
1 224
22.4%
3 44
 
4.4%

restingBP
Real number (ℝ)

Distinct95
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean151.747
Minimum94
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-10-09T23:39:48.197559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum94
5-th percentile101
Q1129
median147
Q3181
95-th percentile197
Maximum200
Range106
Interquartile range (IQR)52

Descriptive statistics

Standard deviation29.965228
Coefficient of variation (CV)0.19746834
Kurtosis-1.0965462
Mean151.747
Median Absolute Deviation (MAD)22
Skewness0.020204353
Sum151747
Variance897.91491
MonotonicityNot monotonic
2023-10-09T23:39:48.518157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127 26
 
2.6%
130 25
 
2.5%
143 23
 
2.3%
126 23
 
2.3%
125 22
 
2.2%
198 21
 
2.1%
195 21
 
2.1%
135 20
 
2.0%
190 20
 
2.0%
142 19
 
1.9%
Other values (85) 780
78.0%
ValueCountFrequency (%)
94 8
0.8%
95 3
 
0.3%
96 7
0.7%
97 9
0.9%
98 6
0.6%
99 5
0.5%
100 10
1.0%
101 8
0.8%
102 6
0.6%
104 9
0.9%
ValueCountFrequency (%)
200 12
1.2%
199 13
1.3%
198 21
2.1%
197 15
1.5%
196 17
1.7%
195 21
2.1%
194 8
 
0.8%
193 14
1.4%
192 8
 
0.8%
191 17
1.7%

serumcholestrol
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct344
Distinct (%)34.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean311.447
Minimum0
Maximum602
Zeros53
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-10-09T23:39:48.813927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1235.75
median318
Q3404.25
95-th percentile526.05
Maximum602
Range602
Interquartile range (IQR)168.5

Descriptive statistics

Standard deviation132.4438
Coefficient of variation (CV)0.4252531
Kurtosis-0.068485696
Mean311.447
Median Absolute Deviation (MAD)85
Skewness-0.30702489
Sum311447
Variance17541.361
MonotonicityNot monotonic
2023-10-09T23:39:49.098337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 53
 
5.3%
268 22
 
2.2%
354 21
 
2.1%
248 19
 
1.9%
336 13
 
1.3%
345 12
 
1.2%
357 12
 
1.2%
325 10
 
1.0%
433 10
 
1.0%
352 10
 
1.0%
Other values (334) 818
81.8%
ValueCountFrequency (%)
0 53
5.3%
85 1
 
0.1%
86 4
 
0.4%
87 2
 
0.2%
132 6
 
0.6%
133 5
 
0.5%
134 3
 
0.3%
135 6
 
0.6%
136 3
 
0.3%
137 9
 
0.9%
ValueCountFrequency (%)
602 2
0.2%
601 3
0.3%
561 2
0.2%
560 1
 
0.1%
559 3
0.3%
558 1
 
0.1%
556 1
 
0.1%
555 1
 
0.1%
554 1
 
0.1%
553 2
0.2%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0
704 
1
296 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 704
70.4%
1 296
29.6%

Length

2023-10-09T23:39:49.380087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-09T23:39:49.616006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 704
70.4%
1 296
29.6%

Most occurring characters

ValueCountFrequency (%)
0 704
70.4%
1 296
29.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 704
70.4%
1 296
29.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 704
70.4%
1 296
29.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 704
70.4%
1 296
29.6%

restingrelectro
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0
454 
1
344 
2
202 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row2

Common Values

ValueCountFrequency (%)
0 454
45.4%
1 344
34.4%
2 202
20.2%

Length

2023-10-09T23:39:49.806829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-09T23:39:50.035440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 454
45.4%
1 344
34.4%
2 202
20.2%

Most occurring characters

ValueCountFrequency (%)
0 454
45.4%
1 344
34.4%
2 202
20.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 454
45.4%
1 344
34.4%
2 202
20.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 454
45.4%
1 344
34.4%
2 202
20.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 454
45.4%
1 344
34.4%
2 202
20.2%

maxheartrate
Real number (ℝ)

Distinct129
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean145.477
Minimum71
Maximum202
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-10-09T23:39:50.262797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum71
5-th percentile85
Q1119.75
median146
Q3175
95-th percentile195.05
Maximum202
Range131
Interquartile range (IQR)55.25

Descriptive statistics

Standard deviation34.190268
Coefficient of variation (CV)0.23502181
Kurtosis-0.88912326
Mean145.477
Median Absolute Deviation (MAD)28
Skewness-0.25115448
Sum145477
Variance1168.9744
MonotonicityNot monotonic
2023-10-09T23:39:50.578739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
186 20
 
2.0%
138 19
 
1.9%
145 19
 
1.9%
168 18
 
1.8%
156 17
 
1.7%
135 15
 
1.5%
139 15
 
1.5%
184 14
 
1.4%
142 14
 
1.4%
175 14
 
1.4%
Other values (119) 835
83.5%
ValueCountFrequency (%)
71 4
0.4%
72 6
0.6%
73 5
0.5%
74 3
0.3%
75 3
0.3%
76 4
0.4%
77 2
 
0.2%
78 4
0.4%
79 2
 
0.2%
80 5
0.5%
ValueCountFrequency (%)
202 8
0.8%
201 7
0.7%
200 6
0.6%
199 9
0.9%
198 8
0.8%
197 2
 
0.2%
196 10
1.0%
195 9
0.9%
194 8
0.8%
193 5
0.5%

exerciseangia
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0
502 
1
498 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 502
50.2%
1 498
49.8%

Length

2023-10-09T23:39:50.852598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-09T23:39:51.081148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 502
50.2%
1 498
49.8%

Most occurring characters

ValueCountFrequency (%)
0 502
50.2%
1 498
49.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 502
50.2%
1 498
49.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 502
50.2%
1 498
49.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 502
50.2%
1 498
49.8%

oldpeak
Real number (ℝ)

Distinct63
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7077
Minimum0
Maximum6.2
Zeros19
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-10-09T23:39:51.611825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2
Q11.3
median2.4
Q34.1
95-th percentile5.7
Maximum6.2
Range6.2
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation1.7207532
Coefficient of variation (CV)0.63550365
Kurtosis-1.0098743
Mean2.7077
Median Absolute Deviation (MAD)1.35
Skewness0.30206616
Sum2707.7
Variance2.9609917
MonotonicityNot monotonic
2023-10-09T23:39:51.928103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.4 35
 
3.5%
1.8 31
 
3.1%
3.2 30
 
3.0%
1.9 29
 
2.9%
1 28
 
2.8%
2.9 27
 
2.7%
0.8 26
 
2.6%
2.3 24
 
2.4%
1.4 23
 
2.3%
2.6 23
 
2.3%
Other values (53) 724
72.4%
ValueCountFrequency (%)
0 19
1.9%
0.1 14
1.4%
0.2 20
2.0%
0.3 20
2.0%
0.4 20
2.0%
0.5 14
1.4%
0.6 20
2.0%
0.7 13
1.3%
0.8 26
2.6%
0.9 16
1.6%
ValueCountFrequency (%)
6.2 6
 
0.6%
6.1 7
 
0.7%
6 9
0.9%
5.9 9
0.9%
5.8 7
 
0.7%
5.7 16
1.6%
5.6 15
1.5%
5.5 13
1.3%
5.4 14
1.4%
5.3 20
2.0%

slope
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2
322 
1
299 
3
199 
0
180 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row1
4th row2
5th row3

Common Values

ValueCountFrequency (%)
2 322
32.2%
1 299
29.9%
3 199
19.9%
0 180
18.0%

Length

2023-10-09T23:39:52.348453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-09T23:39:52.757397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2 322
32.2%
1 299
29.9%
3 199
19.9%
0 180
18.0%

Most occurring characters

ValueCountFrequency (%)
2 322
32.2%
1 299
29.9%
3 199
19.9%
0 180
18.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 322
32.2%
1 299
29.9%
3 199
19.9%
0 180
18.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 322
32.2%
1 299
29.9%
3 199
19.9%
0 180
18.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 322
32.2%
1 299
29.9%
3 199
19.9%
0 180
18.0%

noofmajorvessels
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1
344 
0
275 
2
265 
3
116 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row0
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1 344
34.4%
0 275
27.5%
2 265
26.5%
3 116
 
11.6%

Length

2023-10-09T23:39:53.198113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-09T23:39:53.565007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 344
34.4%
0 275
27.5%
2 265
26.5%
3 116
 
11.6%

Most occurring characters

ValueCountFrequency (%)
1 344
34.4%
0 275
27.5%
2 265
26.5%
3 116
 
11.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 344
34.4%
0 275
27.5%
2 265
26.5%
3 116
 
11.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 344
34.4%
0 275
27.5%
2 265
26.5%
3 116
 
11.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 344
34.4%
0 275
27.5%
2 265
26.5%
3 116
 
11.6%

target
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1
580 
0
420 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 580
58.0%
0 420
42.0%

Length

2023-10-09T23:39:54.009486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-09T23:39:54.440753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 580
58.0%
0 420
42.0%

Most occurring characters

ValueCountFrequency (%)
1 580
58.0%
0 420
42.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 580
58.0%
0 420
42.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 580
58.0%
0 420
42.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 580
58.0%
0 420
42.0%

Interactions

2023-10-09T23:39:43.551467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-09T23:39:33.385998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-09T23:39:35.336614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-09T23:39:36.935226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-09T23:39:39.248348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-09T23:39:41.778309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-09T23:39:43.798587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-09T23:39:34.095593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-09T23:39:35.593061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-09T23:39:37.318401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-09T23:39:39.656942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-09T23:39:42.059385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-09T23:39:44.041873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-09T23:39:34.334999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-09T23:39:35.854443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-09T23:39:37.753352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-09T23:39:40.083751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-09T23:39:42.336489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-09T23:39:44.292413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-09T23:39:34.586696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-09T23:39:36.104505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-09T23:39:38.161292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-09T23:39:40.492428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-09T23:39:42.588288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-09T23:39:44.548875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-09T23:39:34.848478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-09T23:39:36.352136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-09T23:39:38.568516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-09T23:39:40.904735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-09T23:39:42.842913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-09T23:39:44.804250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-09T23:39:35.098766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-09T23:39:36.606154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-09T23:39:38.922590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-09T23:39:41.340673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-09T23:39:43.081895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-10-09T23:39:54.769047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
patientidagerestingBPserumcholestrolmaxheartrateoldpeakgenderchestpainfastingbloodsugarrestingrelectroexerciseangiaslopenoofmajorvesselstarget
patientid1.0000.006-0.0220.049-0.038-0.0140.0000.0000.0660.0450.0890.0000.0200.000
age0.0061.000-0.0200.040-0.044-0.0250.0000.0290.0320.0770.0000.0000.0000.064
restingBP-0.022-0.0201.0000.1480.087-0.0100.2950.3290.1820.2000.0160.2720.1910.555
serumcholestrol0.0490.0400.1481.0000.042-0.0230.2310.1720.3310.1840.0200.2720.1600.526
maxheartrate-0.038-0.0440.0870.0421.0000.0380.1180.1320.1580.0810.0320.1810.1340.364
oldpeak-0.014-0.025-0.010-0.0230.0381.0000.3780.0810.0680.0780.0000.1660.0430.109
gender0.0000.0000.2950.2310.1180.3781.0000.1030.0000.0240.0380.1250.0840.000
chestpain0.0000.0290.3290.1720.1320.0810.1031.0000.2270.1860.0000.2960.1790.575
fastingbloodsugar0.0660.0320.1820.3310.1580.0680.0000.2271.0000.1380.0000.2570.1850.299
restingrelectro0.0450.0770.2000.1840.0810.0780.0240.1860.1381.0000.0000.2610.1550.430
exerciseangia0.0890.0000.0160.0200.0320.0000.0380.0000.0000.0001.0000.0000.0000.021
slope0.0000.0000.2720.2720.1810.1660.1250.2960.2570.2610.0001.0000.3250.853
noofmajorvessels0.0200.0000.1910.1600.1340.0430.0840.1790.1850.1550.0000.3251.0000.522
target0.0000.0640.5550.5260.3640.1090.0000.5750.2990.4300.0210.8530.5221.000

Missing values

2023-10-09T23:39:45.168386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-09T23:39:45.680499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

patientidagegenderchestpainrestingBPserumcholestrolfastingbloodsugarrestingrelectromaxheartrateexerciseangiaoldpeakslopenoofmajorvesselstarget
0103368531217100114705.3331
11192504010942290111503.7110
211937249121331420020215.0100
313251443101382951115303.2221
4146211311119900213605.3321
5148462241117300016104.7321
616868679121302400215702.5211
717049852101273450019214.9100
818822562101213570113802.8000
919252361001901810115002.9201
patientidagegenderchestpainrestingBPserumcholestrolfastingbloodsugarrestingrelectromaxheartrateexerciseangiaoldpeakslopenoofmajorvesselstarget
99098889186301196367029613.2201
99198964382410170354009011.5000
992991170077001832981214212.4331
9939921653250119700019501.0301
994993799862111253420012812.1000
995994954448121393490218315.6221
99699534234713143258119815.7100
997996585969101564341019601.4311
998998850745111864170111715.9321
999999085525101582700014314.7000